Brain Cancer Recurrence Detection Method
- Doctors can cut it out or blast it with radiation, but that only buys time. The cancer has an insidious ability to hide enough tumor cells in tissue...
- patients diagnosed with glioblastoma survive for an average of 15 months.
- The challenge lies in identifying these hidden cancer cells and predicting where the tumor might grow next.
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Predicting Glioblastoma Recurrence with Fluid Dynamics
What is Glioblastoma and Why is it So Difficult to Treat?
Glioblastoma is a devastatingly effective brain cancer. Doctors can cut it out or blast it with radiation, but that only buys time. The cancer has an insidious ability to hide enough tumor cells in tissue around the tumor to allow it to return as deadly as ever.
patients diagnosed with glioblastoma survive for an average of 15 months.
The challenge lies in identifying these hidden cancer cells and predicting where the tumor might grow next. conventional methods frequently enough fail to detect these dispersed cells, leading to inevitable recurrence.
New Research: Identifying Recurrence Pathways
Jennifer Munson and her research team at the Fralin Biomedical Research Institute at VTC believe they have developed a tool to address this critical need.
Their method, described in npj Biomedical Innovations, combines magnetic resonance imaging (MRI), Munson’s expertise in fluid dynamics within human tissues, and a novel algorithm developed by her team to identify and predict where the cancer might reappear.
“If you can’t find the tumor cells, you can’t kill the tumor cells whether that’s by cutting them out, hitting them with radiation therapy, or getting drugs to them,” says Munson, professor and director of the FBRI Cancer Research Center-Roanoke. “This is a method that now we believe can allow us to find those tumor cells.”
How Does the Method Work?
The research leverages the understanding that glioblastoma cells frequently enough spread along pathways of fluid flow within the brain. These pathways aren’t always obvious, but they are crucial for the cancer’s dissemination.
- MRI Scanning: High-resolution MRI scans are used to visualize the tumor and surrounding brain tissue.
- Fluid Dynamics Modeling: Munson’s expertise in fluid dynamics is applied to model how fluid moves through and around the tumor. This modeling considers the complex architecture of the brain and the properties of the fluid itself.
- Algorithm submission: The team’s algorithm analyzes the fluid flow patterns identified in the MRI scans. It identifies areas where cancer cells are most likely to accumulate and form new tumors.
- Prediction of Recurrence: the algorithm generates a prediction map, highlighting areas at high risk of recurrence.
Implications and Future Directions
This new method has the potential to significantly improve glioblastoma treatment by allowing doctors to target residual cancer cells more effectively. By predicting where the tumor is likely to recur, clinicians can focus radiation therapy or chemotherapy on those specific areas, minimizing